Christian Marinaccio, PhD Candidate, of Northwestern University, describes research he is conducting in the laboratory of John D. Crispino, PhD, which shows the loss of the tumor suppressor gene LKB1/STK11 facilitates progression of myeloproliferative neoplasms to acute myeloid leukemia (Abstract 1).
David T. Teachey, MD, of the University of Pennsylvania and Children’s Hospital of Philadelphia, discusses data showing that cranial radiation might be eliminated in most children with T-cell acute lymphoblastic leukemia and that bortezomib may improve survival in children with T-cell lymphoblastic lymphoma (Abstract 266).
Smita Bhatia, MD, MPH, and Radhika Gangaraju, MD, both of the Institute for Cancer Outcomes and Survivorship, University of Alabama at Birmingham, discuss findings that showed survivors of bone marrow transplants are at a 7- to 12-fold higher risk of coronary heart disease than a sibling comparison group. They recommend aggressive management of cardiovascular risk factors to prevent morbidity from heart disease in this patient population (Abstract 73).
Steven M. Horwitz, MD, of Memorial Sloan Kettering Cancer Center, discusses phase II data from the Primo trial, which support continued evaluation of duvelisib as a treatment option for relapsed or refractory peripheral T-cell lymphoma due to consistent response rates (Abstract 44).
Nitin Jain, MD, of The University of Texas MD Anderson Cancer Center, reviews six important abstracts on CAR T-cell treatments for B-cell acute lymphoblastic leukemia (ALL): successful 24-hour manufacture of CAR T-cell therapy; ALLCAR19, a novel fast-off rate therapy; donor-derived CD19-targeted treatment; CAR 2.0 therapy to manage post-transplant relapse; UCART22, allogeneic engineered T cells expressing anti-CD22 chimeric antigen receptor; and inotuzumab ozogamicin in pediatric CD-22–positive disease (Session 614, Abstracts 159-164).
Hassan Awada, MD, of the Taussig Cancer Institute, Cleveland Clinic Foundation, discusses the use of newer machine-learning techniques to help decipher a set of prognostic subgroups that could predict survival, thus potentially improving on traditional methods and moving acute myeloid leukemia into the era of personalized medicine (Abstract 34).
